Import data depending on backend

We want to use data from the action if we run this using the OpenSAFELY pipeline/backend. But this also allows us to use released ouputs to generate the report if we run this code in our local development environment.

reports/pharmacy_first_report.Rmd

@@ -19,10 +19,18 @@ library(readr)
 source(here::here("lib", "functions", "function_tidy_measures.R"))
 source(here::here("lib", "functions", "function_plot_measures.R"))
 
-# Load data
-df_measures <- readr::read_csv(
-  here::here("output", "measures", "pf_codes_conditions_measures.csv")
-)
+# Load data based on environment
+if (Sys.getenv("OPENSAFELY_BACKEND") != "") {
+  # Load data from generate_pf_measures action
+  df_measures <- readr::read_csv(
+    here::here("output", "measures", "pf_codes_conditions_measures.csv")
+  )
+} else {
+  # Load data from released_output directory
+  df_measures <- readr::read_csv(
+    here::here("released_output", "measures", "pf_codes_conditions_measures.csv")
+  )
+}
 
 # Define dictionaries with tidy names and mappings for measures
 pf_measures_name_dict <- list(
@@ -72,26 +80,32 @@ df_measures <- tidy_measures(
 
 df_measures$ethnicity <- factor(
   df_measures$ethnicity,
-  levels = c("White", "Mixed", "Asian or Asian British",
-  "Black or Black British", "Chinese or Other Ethnic Groups",
-  "Missing"), 
+  levels = c(
+    "White", "Mixed", "Asian or Asian British",
+    "Black or Black British", "Chinese or Other Ethnic Groups",
+    "Missing"
+  ),
   ordered = TRUE
 )
 
 df_measures$age_band <- factor(
   df_measures$age_band,
-  levels = c("0-19", "20-39", "40-59",
-  "60-79", "80+",
-  "Missing"), 
+  levels = c(
+    "0-19", "20-39", "40-59",
+    "60-79", "80+",
+    "Missing"
+  ),
   ordered = TRUE
 )
 
 df_measures$region <- factor(
   df_measures$region,
-  levels = c("East", "East Midlands", "London",
-  "North East", "North West", "South East",
-  "South West", "West Midlands", "Yorkshire and The Humber",
-  "Missing"), 
+  levels = c(
+    "East", "East Midlands", "London",
+    "North East", "North West", "South East",
+    "South West", "West Midlands", "Yorkshire and The Humber",
+    "Missing"
+  ),
   ordered = TRUE
 )
 
@@ -234,8 +248,6 @@ plot_measures(
 ### Clinical Services by ethnicity
 
 ```{r, message=FALSE, warning=FALSE}
-
-
 # Select measures and breakdown
 df_measures_selected <- df_measures %>%
   filter(measure_desc == "pharmacy_first_services") %>%
@@ -372,7 +384,6 @@ plot_measures(
 ### Clinical Conditions by ethnicity
 
 ```{r, message=FALSE, warning=FALSE, fig.height=15, fig.width=8}
-
 # Select measures and breakdown
 df_measures_selected <- df_measures %>%
   filter(measure_desc == "clinical_condition") %>%